Text Classification of International Relations Based on Deep Learning

With the rapid development of global information and the popularization of the Internet, this new method of information dissemination has changed people's lifestyles, but Internet data as an information carrier has also shown explosive growth. Effectively organizing and extracting these data to find the information that users need faster, more accurately, and more comprehensively is a major challenge facing the field of informatics today. Text mining mainly includes methods such as text sorting, text grouping, and document summarization. Text sorting is a very important way for people to acquire knowledge and recognize things. It plays an important role in natural language editing, language understanding, machine learning, and topic recognition. Therefore, this article focuses on the deep learning of international relations text classification. The learned international relations text classification model is constructed, and the constructed model is tested. The test results show that the classification model proposed in this paper has an average classification accuracy of 90%, which is better than other classification models.